The following is an extract from the article Why Computers May Never Think Like People by Hubert and Stuart Dreyfus. First published in the MIT Technology Review, I found the essay in the book Knowledge Management Tools, a collection of essays edited by Rudy Ruggles.
One of us, Stuart, knows all too well the difference between expert and merely competent chess players; he is stuck at the competent level. He took up chess as an outlet for his analytical talent in mathematics, and most of the other players on his college team were also mathematicians. At some point, a few of his team mates who were not mathematicians began to play fast five- or ten-minute games of chess, and also began eagerly to reply the great games of the grand masters. But Stuart and his mathematical colleagues resisted because fast chess didn’t give them the time to figure out what to do. They also felt that they could learn nothing from the grandmaster games, since the record of those games seldom if ever provided specific rules and principles.
Some of his teammates who played fast chess and studied grand-master games absorbed a great deal of concrete experience and went on to become chess masters. Yet Stuart and his mathematical friends never got beyond the competent level. Students of math may predominate among chess enthusiasts, but a truck driver is as likely as a mathematician to be among the world’s best players. Stuart says that he is glad that his analytical approach to chess stymied his progress because it helped him to see that there is more to skill than reasoning.
